Selection of Optimal Denoising Filter Using Quality Assessment for Potentially Lethal Optical Wound Images

Abstract The objective of this paper is to select the best filter for the camera-captured digital wound image pre-processing. Wound images give the most essential information about the wound. The information may be size, status of the wound, tissue composition and rate of healing. These images are most often corrupted by impulse or random noises while capturing them. Corrupted images often suffer from sharpness, chrominance and luminance. Application of several filtering schemes such as linear and non-linear filtering suppresses noise and improves the image quality. In this paper, a comparative study of five filters has been performed using mathematical morphology operations for removing the impulse/random noise. These five filters were applied on seventy-five randomly selected wound images from the developed image database as well as online chronic wound image database. In order to assess the quality of the filtered image, seven quality measures have been applied. Local first order statistics (LFOS) is the best and efficient filter in the context of reduced mean square error (MSE) and high peak signal to noise ratio (PSNR) between the reference original and the filtered image.

[1]  J. Chatterjee,et al.  Comparative evaluation of speckle reduction algorithms in optical coherence tomography , 2010, 2010 Annual IEEE India Conference (INDICON).

[2]  Gargi Mukherjee,et al.  Segmentation of Chronic Wound Areas by Clustering Techniques Using Selected Color Space , 2013 .

[3]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[4]  E.Y. Lam,et al.  Combining gray world and retinex theory for automatic white balance in digital photography , 2005, Proceedings of the Ninth International Symposium on Consumer Electronics, 2005. (ISCE 2005)..

[5]  Alan C. Bovik,et al.  Visual Importance Pooling for Image Quality Assessment , 2009, IEEE Journal of Selected Topics in Signal Processing.

[6]  R. Lotufo,et al.  Morphological Image Processing , 2008 .

[7]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[8]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[9]  S. Indu,et al.  Image Fusion Algorithm for Impulse Noise Reduction , 2009, 2009 International Conference on Advances in Recent Technologies in Communication and Computing.

[10]  Tamer F. Rabie,et al.  Adaptive hybrid mean and median filtering of high-ISO long-exposure sensor noise for digital photography , 2004, J. Electronic Imaging.

[11]  Chandan Chakraborty,et al.  Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment , 2014, BioMed research international.

[12]  Jyotirmoy Chatterjee,et al.  Image quality assessment for performance evaluation of despeckle filters in Optical Coherence Tomography of human skin , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[13]  Chandan Chakraborty,et al.  Automated leukocyte recognition using fuzzy divergence. , 2010, Micron.

[14]  Dingrong Yi,et al.  Detecting early stage pressure ulcer on dark skin using multispectral imager , 2009, International Symposium on Multispectral Image Processing and Pattern Recognition.

[15]  Thomas W. Parks,et al.  Image denoising using total least squares , 2006, IEEE Transactions on Image Processing.

[16]  Chun-Ling Yang,et al.  Gradient-Based Structural Similarity for Image Quality Assessment , 2006, 2006 International Conference on Image Processing.